Forecasting new student enrollment numbers using simple linear regression - Seroan, Miracle; Sulistyaningsih, Murni; Pitoy, Cori (Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika, 2025)
This study aims to forecast the number of new students at Manado State University using historical enrollment data from the past five years (2020–2024). A simple linear regression model was developed based on this data and subsequently used to predict new student admissions for the next five academic years, from 2025/2026 to 2029/2030. The results indicate that the simple linear regression method is sufficiently effective for predicting new student enrollment, despite certain limitations inherent in linear modeling. Model performance was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). At the university level, the forecasting results yielded a MAPE value of 5.7% and an MSE of 31,018.64, indicating high predictive accuracy. At the faculty level, MAPE and MSE values varied, with the Faculty of Languages and Arts (3.92% and 574), Faculty of Engineering (5.55% and 4,512.4), Faculty of Economics and Business (7.23% and 11,104.3), Faculty of Mathematics, Natural Sciences, and Earth Sciences (7.32% and 1,421.1), Faculty of Social and Legal Sciences (8.52% and 9,619.2), Faculty of Sports Science and Public Health (18.84% and 29,529.2), and Faculty of Education and Psychology (23.08% and 74,977.2). These findings suggest that simple linear regression can serve as a practical and data-driven tool to support strategic planning and decision-making in new student admissions at Manado State University.